OpenRaiser/Intern-Atlas huggingface dataset:
New Instantiations of the CRYPTO 2017 Masking Schemes
Venue: arXiv cs.CR 2018
Year: 2018

At CRYPTO 2017, Belaïd et al presented two new private multiplication algorithms over finite fields, to be used in secure masking schemes. To date, these algorithms have the lowest known complexity in terms of bilinear multiplication and random masks respectively, both being linear in the number of shares $d+1$. Yet, a practical drawback of both algorithms is that their safe instantiation relies on finding matrices satisfying certain conditions. In their work, Belaïd et al only address these up to $d=2$ and 3 for the first and second algorithm respectively, limiting so far the practical usefulness of their schemes. In this paper, we use in turn an algebraic, heuristic, and experimental approach to find many more safe instances of Belaïd et al's algorithms. This results in explicit such instantiations up to order $d = 6$ over large fields, and up to $d = 4$ over practically relevant fields such as $\mathbb{F}_{2^8}$.
Efficient Auctions With Common Values
Venue: arXiv cs.GT 2018
Year: 2018

Consider the problem of allocating goods to buyers through an auction. An auction is efficient if the resulting allocation maximizes total welfare, conditional on the information available. If buyers have private values, the Vickrey-Groves-Clarke mechanism is efficient. If buyers have common values and a buyer's information can be summarized as a one-dimensional signal, Dasgupta and Maskin present an efficient auction. We construct an efficient auction mechanism in case buyer information is multidimensional, for a restricted class of valuation functions, and we prove which of the assumptions made are necessary for the existence of an efficient mechanism.
DRAPS: Dynamic and Resource-Aware Placement Scheme for Docker Containers in a Heterogeneous Cluster
Venue: arXiv cs.DC 2018
Year: 2018

Virtualization is a promising technology that has facilitated cloud computing to become the next wave of the Internet revolution. Adopted by data centers, millions of applications that are powered by various virtual machines improve the quality of services. Although virtual machines are well-isolated among each other, they suffer from redundant boot volumes and slow provisioning time. To address limitations, containers were born to deploy and run distributed applications without launching entire virtual machines. As a dominant player, Docker is an open-source implementation of container technology. When managing a cluster of Docker containers, the management tool, Swarmkit, does not take the heterogeneities in both physical nodes and virtualized containers into consideration. The heterogeneity lies in the fact that different nodes in the cluster may have various configurations, concerning resource types and availabilities, etc., and the demands generated by services are varied, such as CPU-intensive (e.g. Clustering services) as well as memory-intensive (e.g. Web services). In this paper, we target on investigating the Docker container cluster and developed, DRAPS, a resource-aware placement scheme to boost the system performance in a heterogeneous cluster.
Stamp-it: A more Thread-efficient, Concurrent Memory Reclamation Scheme in the C++ Memory Model
Venue: arXiv cs.DC 2018
Year: 2018

We present Stamp-it, a new, concurrent, lock-less memory reclamation scheme with amortized, constant-time (thread-count independent) reclamation overhead. Stamp-it has been implemented and proved correct in the C++ memory model using as weak memory-consistency assumptions as possible. We have likewise (re)implemented six other comparable reclamation schemes. We give a detailed performance comparison, showing that Stamp-it performs favorably (sometimes better, at least as good as) than most of these other schemes while being able to reclaim free memory nodes earlier.
Convexity Shape Prior for Level Set based Image Segmentation Method
Venue: arXiv cs.CV 2018
Year: 2018

We propose a geometric convexity shape prior preservation method for variational level set based image segmentation methods. Our method is built upon the fact that the level set of a convex signed distanced function must be convex. This property enables us to transfer a complicated geometrical convexity prior into a simple inequality constraint on the function. An active set based Gauss-Seidel iteration is used to handle this constrained minimization problem to get an efficient algorithm. We apply our method to region and edge based level set segmentation models including Chan-Vese (CV) model with guarantee that the segmented region will be convex. Experimental results show the effectiveness and quality of the proposed model and algorithm.
Presheaf Models of Relational Modalities in Dependent Type Theory
Venue: arXiv cs.LO 2018
Year: 2018

This report is an extension of 'A Model of Parametric Dependent Type Theory in Bridge/Path Cubical Sets' (Nuyts, arXiv:1706.04383). The purpose of this text is to prove all technical aspects of our model for dependent type theory with parametric quantifiers (Nuyts, Vezzosi and Devriese, 2017) and with degrees of relatedness (Nuyts and Devriese, 2018).
Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data
Venue: arXiv cs.CV 2018
Year: 2018

We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial context. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling multiple volumetric heatmaps to be predicted concurrently without prohibitive GPU memory requirements. Further, the system allows inter-landmark spatial relationships to be exploited using a simple overdetermined affine mapping that is robust to detection failures and occlusion or partial views. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 human annotators, the neural network reaches an accuracy which matches the annotator variability, with similar human and machine patterns of variability across landmark classes.
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we describe our approach for the OMG- Emotion Challenge 2018. The goal is to produce utterance-level valence and arousal estimations for videos of approximately 1 minute length. We tackle this problem by first extracting facial expressions features of videos as time series data, and then using Recurrent Neural Networks of the Echo State Network type to model the correspondence between the time series data and valence-arousal values. Experimentally we show that the proposed approach surpasses the baseline methods provided by the organizers.
Image Based Fashion Product Recommendation with Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style. For that purpose, a neural network classifier is used as a data-driven, visually-aware feature extractor. The latter then serves as input for similarity-based recommendations using a ranking algorithm. Our approach is tested on the publicly available Fashion dataset. Initialization strategies using transfer learning from larger product databases are presented. Combined with more traditional content-based recommendation systems, our framework can help to increase robustness and performance, for example, by better matching a particular customer style.
Automatic Data Registration of Geostationary Payloads for Meteorological Applications at ISRO
Venue: arXiv cs.CV 2018
Year: 2018

The launch of KALPANA-1 satellite in the year 2002 heralded the establishment of an indigenous operational payload for meteorological predictions. This was further enhanced in the year 2003 with the launching of INSAT-3A satellite. The software for generating products from the data of these two satellites was taken up subsequently in the year 2004 and the same was installed at the Indian Meteorological Department, New Delhi in January 2006. Registration has been one of the most fundamental operations to generate almost all the data products from the remotely sensed data. Registration is a challenging task due to inevitable radiometric and geometric distortions during the acquisition process. Besides the presence of clouds makes the problem more complicated. In this paper, we present an algorithm for multitemporal and multiband registration. In addition, India facing reference boundaries for the CCD data of INSAT-3A have also been generated. The complete implementation is made up of the following steps: 1) automatic identification of the ground control points (GCPs) in the sensed data, 2) finding the optimal transformation model based on the match-points, and 3) resampling the transformed imagery to the reference coordinates. The proposed algorithm is demonstrated using the real datasets from KALPANA-1 and INSAT-3A. Both KALAPANA-1 and INSAT-3A have recently been decommissioned due to lack of fuel, however, the experience gained from them have given rise to a series of meteorological satellites and associated software; like INSAT-3D series which give continuous weather forecasting for the country. This paper is not so much focused on the theory (widely available in the literature) but concentrates on the implementation of operational software.
CascadeCNN: Pushing the performance limits of quantisation
Venue: arXiv cs.CV 2018
Year: 2018

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, to perform high-throughput inference by exploiting the computation time-accuracy trade-off. Without the need for retraining, a two-stage architecture tailored for any given FPGA device is generated, consisting of a low- and a high-precision unit. A confidence evaluation unit is employed between them to identify misclassified cases at run time and forward them to the high-precision unit or terminate computation. Experiments demonstrate that CascadeCNN achieves a performance boost of up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy.
A scene perception system for visually impaired based on object detection and classification using multi-modal DCNN
Venue: arXiv cs.CV 2018
Year: 2018

This paper represents a cost-effective scene perception system aimed towards visually impaired individual. We use an odroid system integrated with an USB camera and USB laser that can be attached on the chest. The system classifies the detected objects along with its distance from the user and provides a voice output. Experimental results provided in this paper use outdoor traffic scenes. The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.
Aperiodic points in $\mathbb Z^2$-subshifts
Venue: arXiv cs.DM 2018
Year: 2018

We consider the structure of aperiodic points in $\mathbb Z^2$-subshifts, and in particular the positions at which they fail to be periodic. We prove that if a $\mathbb Z^2$-subshift contains points whose smallest period is arbitrarily large, then it contains an aperiodic point. This lets us characterise the computational difficulty of deciding if an $\mathbb Z^2$-subshift of finite type contains an aperiodic point. Another consequence is that $\mathbb Z^2$-subshifts with no aperiodic point have a very strong dynamical structure and are almost topologically conjugate to some $\mathbb Z$-subshift. Finally, we use this result to characterize sets of possible slopes of periodicity for $\mathbb Z^3$-subshifts of finite type.
CUDACLAW: A high-performance programmable GPU framework for the solution of hyperbolic PDEs
Venue: arXiv cs.MS 2018
Year: 2018

We present cudaclaw, a CUDA-based high performance data-parallel framework for the solution of multidimensional hyperbolic partial differential equation (PDE) systems, equations describing wave motion. cudaclaw allows computational scientists to solve such systems on GPUs without being burdened by the need to write CUDA code, worry about thread and block details, data layout, and data movement between the different levels of the memory hierarchy. The user defines the set of PDEs to be solved via a CUDA- independent serial Riemann solver and the framework takes care of orchestrating the computations and data transfers to maximize arithmetic throughput. cudaclaw treats the different spatial dimensions separately to allow suitable block sizes and dimensions to be used in the different directions, and includes a number of optimizations to minimize access to global memory.
Logchain: Blockchain-assisted Log Storage
Venue: arXiv cs.CR 2018
Year: 2018

During the normal operation of a Cloud solution, no one usually pays attention to the logs except technical department, which may periodically check them to ensure that the performance of the platform conforms to the Service Level Agreements. However, the moment the status of a component changes from acceptable to unacceptable, or a customer complains about accessibility or performance of a platform, the importance of logs increases significantly. Depending on the scope of the issue, all departments, including management, customer support, and even the actual customer, may turn to logs to find out what has happened, how it has happened, and who is responsible for the issue. The party at fault may be motivated to tamper the logs to hide their fault. Given the number of logs that are generated by the Cloud solutions, there are many tampering possibilities. While tamper detection solution can be used to detect any changes in the logs, we argue that critical nature of logs calls for immutability. In this work, we propose a blockchain-based log system, called Logchain, that collects the logs from different providers and avoids log tampering by sealing the logs cryptographically and adding them to a hierarchical ledger, hence, providing an immutable platform for log storage.
Soteria: Automated IoT Safety and Security Analysis
Venue: arXiv cs.CR 2018
Year: 2018

Broadly defined as the Internet of Things (IoT), the growth of commodity devices that integrate physical processes with digital systems have changed the way we live, play and work. Yet existing IoT platforms cannot evaluate whether an IoT app or environment is safe, secure, and operates correctly. In this paper, we present Soteria, a static analysis system for validating whether an IoT app or IoT environment (collection of apps working in concert) adheres to identified safety, security, and functional properties. Soteria operates in three phases; (a) translation of platform-specific IoT source code into an intermediate representation (IR), (b) extracting a state model from the IR, (c) applying model checking to verify desired properties. We evaluate Soteria on 65 SmartThings market apps through 35 properties and find nine (14%) individual apps violate ten (29%) properties. Further, our study of combined app environments uncovered eleven property violations not exhibited in the isolated apps. Lastly, we demonstrate Soteria on MalIoT, a novel open-source test suite containing 17 apps with 20 unique violations.
Multi-task Maximum Entropy Inverse Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.
Rate-Maximizing OFDM Pilot Patterns for UAV Communications in Nonstationary A2G Channels
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we propose and evaluate rate-maximizing pilot configurations for Unmanned Aerial Vehicle (UAV) communications employing OFDM waveforms. OFDM relies on pilot symbols for effective communications. We formulate a rate-maximization problem in which the pilot spacing (in the time-frequency resource grid) and power is varied as a function of the time-varying channel statistics. The receiver solves this rate-maximization problem, and the optimal pilot spacing and power are explicitly fed back to the transmitter to adapt to the time-varying channel statistics in an air-to-ground (A2G) environment. We show the enhanced throughput performance of this scheme for UAV communications in sub-6 GHz bands. These performance gains are achieved at the cost of very low computational complexity and feedback requirements, making it attractive for A2G UAV communications in 5G.
Teacher's Perception in the Classroom
Venue: arXiv cs.CV 2018
Year: 2018

The ability for a teacher to engage all students in active learning processes in classroom constitutes a crucial prerequisite for enhancing students achievement. Teachers' attentional processes provide important insights into teachers' ability to focus their attention on relevant information in the complexity of classroom interaction and distribute their attention across students in order to recognize the relevant needs for learning. In this context, mobile eye tracking is an innovative approach within teaching effectiveness research to capture teachers' attentional processes while teaching. However, analyzing mobile eye-tracking data by hand is time consuming and still limited. In this paper, we introduce a new approach to enhance the impact of mobile eye tracking by connecting it with computer vision. In mobile eye tracking videos from an educational study using a standardized small group situation, we apply a state-ofthe-art face detector, create face tracklets, and introduce a novel method to cluster faces into the number of identity. Subsequently, teachers' attentional focus is calculated per student during a teaching unit by associating eye tracking fixations and face tracklets. To the best of our knowledge, this is the first work to combine computer vision and mobile eye tracking to model teachers' attention while instructing.
Coded Caching via Line Graphs of Bipartite Graphs
Venue: arXiv cs.IT 2018
Year: 2018

We present a coded caching framework using line graphs of bipartite graphs. A clique cover of the line graph describes the uncached subfiles at users. A clique cover of the complement of the square of the line graph gives a transmission scheme that satisfies user demands. We then define a specific class of such caching line graphs, for which the subpacketization, rate, and uncached fraction of the coded caching problem can be captured via its graph theoretic parameters. We present a construction of such caching line graphs using projective geometry. The presented scheme has a rate bounded from above by a constant with subpacketization level $q^{O((log_qK)^2)}$ and uncached fraction $Θ(\frac{1}{\sqrt{K}})$, where $K$ is the number of users and $q$ is a prime power. We also present a subpacketization-dependent lower bound on the rate of coded caching schemes for a given broadcast setup.
Tool Exchangeable Grasp/Assembly Planner
Venue: arXiv cs.RO 2018
Year: 2018

This paper proposes a novel assembly planner for a manipulator which can simultaneously plan assembly sequence, robot motion, grasping configuration, and exchange of grippers. Our assembly planner assumes multiple grippers and can automatically selects a feasible one to assemble a part. For a given AND/OR graph of an assembly task, we consider generating the assembly graph from which assembly motion of a robot can be planned. The edges of the assembly graph are composed of three kinds of paths, i.e., transfer/assembly paths, transit paths and tool exchange paths. In this paper, we first explain the proposed method for planning assembly motion sequence including the function of gripper exchange. Finally, the effectiveness of the proposed method is confirmed through some numerical examples and a physical experiment.
Particle Filter Networks with Application to Visual Localization
Venue: arXiv cs.RO 2018
Year: 2018

Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or rich sensory inputs such as camera images. This paper introduces the Particle Filter Network (PFnet), which encodes both a system model and a particle filter algorithm in a single neural network. The PF-net is fully differentiable and trained end-to-end from data. Instead of learning a generic system model, it learns a model optimized for the particle filter algorithm. We apply the PF-net to a visual localization task, in which a robot must localize in a rich 3-D world, using only a schematic 2-D floor map. In simulation experiments, PF-net consistently outperforms alternative learning architectures, as well as a traditional model-based method, under a variety of sensor inputs. Further, PF-net generalizes well to new, unseen environments.
On the Formal Model for IEC 61499 Composite Function Blocks
Venue: arXiv cs.DC 2018
Year: 2018

The applications for IEC 61499 that is standard architecture for developing the applications of distributed control and measurement in factory automation, have the connected structure of the graphical elements called BFB(basic function block), SIFB(service interface function block) and CFB(composite function block). The research on the composite function block has been regarded as important issues in implementing hierarchy, multi-functionality and simplicity of software. Nowadays many researchers have been investigated IEC61499 in the fields of the software modeling composed of basic function block and service interface function block, the transformation from IEC61131 to IEC61499 and syntactic extension of ECC of basic function block. However, work related to the mathematical modeling for IEC61499 composite function block using in designing software with hierarchical structure is still lacking. This paper presents the mathematical model for the structure and execution analysis of IEC 61499 composite function blocks by using notation of the set theory. Also a subaplication configuration algorithm is suggested for the subapplication corresponding to the composite function block. Then its effectiveness through the computation experiment of several distributed control applications is shown. The proposed model can be used effectively as a basis for analyzing a runtime environment of a software tool for designing and developing the applications.